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Register a new userRecurrent Attention for Neural Machine Translation
الاهتمام المتكرر للترجمة الآلية العصبية
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أسئلة البحث الحديثة أهمية الاهتمام الذاتي لمنتج المنتج في نماذج المحولات ويظهر أن معظم رؤساء الاهتمام تعلم أنماطا موضعية بسيطة. في هذه الورقة، ندفع أبعد من ذلك في خط البحث هذا واقتراح آلية بديلة جديدة عن النفس: الاهتمام المتكرر (ران). تتعلم RAN بشكل مباشر أوزان الاهتمام دون أي تفاعل رمزي إلى رمز ويحسن قدرتها على تفاعل الطبقة إلى الطبقة. عبر مجموعة واسعة من التجارب في 10 مهام ترجمة آلية، نجد أن نماذج RAN تنافسية وتفوق نظيرها المحول في بعض السيناريوهات، مع عدد أقل من المعلمات ووقت الاستدلال. خاصة، عند تطبيق ركض إلى فك ترميز المحولات، يجلب التحسينات المتسقة عن طريق حوالي +0.5 بلو في 6 مهام الترجمة و +1.0 Bleu على مهمة الترجمة التركية الإنجليزية. بالإضافة إلى ذلك، نجرينا تحليلا مكثفا بشأن أوزان الاهتمام في ركض لتأكيد المعقولية. ران لدينا هو بديل واعد لبناء نماذج NMT أكثر فعالية وكفاءة.
Recent research questions the importance of the dot-product self-attention in Transformer models and shows that most attention heads learn simple positional patterns. In this paper, we push further in this research line and propose a novel substitute mechanism for self-attention: Recurrent AtteNtion (RAN) . RAN directly learns attention weights without any token-to-token interaction and further improves their capacity by layer-to-layer interaction. Across an extensive set of experiments on 10 machine translation tasks, we find that RAN models are competitive and outperform their Transformer counterpart in certain scenarios, with fewer parameters and inference time. Particularly, when apply RAN to the decoder of Transformer, there brings consistent improvements by about +0.5 BLEU on 6 translation tasks and +1.0 BLEU on Turkish-English translation task. In addition, we conduct extensive analysis on the attention weights of RAN to confirm their reasonableness. Our RAN is a promising alternative to build more effective and efficient NMT models.
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